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If you cannot find the sensor you need in any
manufacturer’s catalogue then you can
probably make your own – in software.
This is the basic premise behind sensor
fusion. The idea is that if you combine the
data from a variety of different sensors, you
will be able to measure parameters for which
no single sensor exists.

An easy example is the ‘‘electronic nose’’.
These typically comprise an array of 20 or
more chemical sensors, each with its own
sensitivity to a particular gas or combination
of gases. Each sensor is pretty crude and lacks
selectivity, however it only needs to have a
repeatable set of characteristics for it to be a
useful addition to the nose.

It is the combination of various
characteristics that enables the nose to
discriminate between aromas. And as I
cannot believe that evolution provided us with
a specific sensor for Chateau Lafitte 1992, I
can only presume that our own noses operate
in much the same way.

Another even simpler example is the
determination of acceleration from a distance
sensor and an accurate time signal. This is
very easy and straightforward because all the
data are instantaneously available and it all
relates to the same instant in time.
Difficulties arise if one sensor has a delayed
response compared with those of the rest of
the system; and these difficulties are
compounded by additional non-linearities.
For example, if you wish to determine the
acceleration of a model train, but your only
inputs are time and a ‘‘distance’’ encoder
which is attached to a carriage which is
connected to the train in front by a rubber
band; then life suddenly becomes a lot more
complicated.

Sensor fusion has a great deal of potential,
but getting a valid result can be far from
straightforward. Anyone who thinks that they
can simply connect a large number of sensors
to a neural network and press the ‘‘learn’’
button, is likely to be disappointed by the
result.

‘‘Garbage in garbage out’’ is especially valid
when multiple sensors are involved. Each
sensor input must first be cleaned up, delayed
by some amount which may also be a variable,
and then weighted by the network in
accordance with its significance to the end
result. Finally you need to test the whole
system with a large variety of real data to
make sure that it actually works, and then
cross your fingers.

Fundamental to all the above is that you
must understand how your system works. If
you do not have this knowledge and instead
abdicate responsibility to an artificial neural
network that has no idea whether it is
monitoring the acceleration of a train or the
temperature in a furnace, then you will
deserve the end result. This is a shame
because sensor fusion does have a lot to offer,
but it needs to be used with great care.